Stiffness Evaluation of Aortic Aneurysms Using an Ultrafast Principal Strain Estimator: In Vitro Validation
Bibliographic record
Abstract
Aortic stiffness is altered by cardiovascular diseases and exacerbated in innate or pathological conditions, which might be detected by noninvasive vascular elastography. However, accuracy and robustness of conventional elastography with low frame rate and limited lateral resolution are expected to deteriorate due to the rapid motion and large 3D deformation of the aortic arch. Considering that tissue-Doppler imaging (TDI) is advantageous for large deformation conditions and optical flow (OF) tracking is accurate for small motions, an ultrafast regularized TDI-OF principal strain estimator is proposed to evaluate aortic stiffness in vitro. Two aorta-mimicking phantom models were designed and driven by a hydraulic pump to simulate wall deformations under normal and pathological aortic aneurysm conditions. Deformation data were recorded by ultrafast diverging echoes using a Verasonics platform equipped with a 2.5 MHz phased array transducer (frame rate: 4500 Hz). Contrast and resolution were enhanced by coherent compounding with TDI motion compensation. Aortic principal strain maps and regional strain curves were then estimated by using the proposed model, which was modified by a regularization strategy and treated as a least-squares problem to improve the estimation robustness. The aortic stiffness was evaluated using the 2D principal strain maps in systolic and diastolic phases. Accumulated strain curves of superior and inferior aortic walls were also documented. In vitro principal strain ranges were smaller in the case of the aortic aneurysm compared with normal aorta. Heterogeneous strain patterns were also observed. These results suggest that the proposed model could detect and evaluate aortic aneurysm stiffness and may be useful clinically for the early and timely detection of degraded mechanical properties to impact patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".